Citation

Zhang Y, Li C, Wei C, Cheng R, Lv T, Wang J, Zhao C, Wang Z, Li F, Peng X, Xu M, Dong K. InfoMat 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, John Wiley and Sons)

DOI

10.1002/inf2.12534

PMID

unavailable

Abstract

The inherent unpredictability of the maritime environment leads to low rates of survival during accidents. Life jackets serve as a crucial safety measure in underwater environments. Nonetheless, most conventional life jackets lack the capability to monitor the wearer's underwater body movements, impeding their effectiveness in rescue operations. Here, we present an intelligent self-powered life jacket system (SPLJ) composed of a wireless body area sensing network, a set of deep learning analytics, and a human condition detection platform. Six coaxial core-shell structure triboelectric fiber sensors with high sensitivity, stretchability, and flexibility are integrated into this system. Additionally, a portable integrated circuit module is incorporated into the SPLJ to facilitate real-time monitoring of the wearer's movement. Moreover, by leveraging the deep-learning-assisted data analytics and establishing a robust correlation between the wearer's movements and condition, we have developed a comprehensive system for monitoring drowning individuals, achieving an outstanding recognition accuracy of 100%. This groundbreaking work introduces a fresh approach to underwater intelligent survival devices, offering promising prospects for advancing underwater smart wearable devices in rescue operations and the development of ocean industry.

Language: en

Keywords

deep learning; intelligent life jackets; movement recognition; self-powered; triboelectric fiber sensors